Class-specific attribute weighted naive Bayes
作者：
Highlights：
• Almost all existing attribute weighting approaches to naive Bayes are class-independent.
• We propose a new class-specific attribute weighting paradigm for naive Bayes.
• The resulting model is called class-specific attribute weighted naive Bayes (CAWNB).
• To learn CAWNB, we propose two gradient-based learning algorithms.
• The experimental results validate the effectiveness of the proposed algorithms.
摘要
•Almost all existing attribute weighting approaches to naive Bayes are class-independent.•We propose a new class-specific attribute weighting paradigm for naive Bayes.•The resulting model is called class-specific attribute weighted naive Bayes (CAWNB).•To learn CAWNB, we propose two gradient-based learning algorithms.•The experimental results validate the effectiveness of the proposed algorithms.
论文关键词：Naive Bayes,Attribute weighting,Weight optimization
论文评审过程：Received 13 June 2018, Revised 21 October 2018, Accepted 27 November 2018, Available online 28 November 2018, Version of Record 30 November 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.032
